Ecosystème

Exemple : Céréales

Column

Carte des catégories

Column

Tableau de mesure de distance avec le centroïde de l’écosystème

Cartographie des secteurs

Column

Cartographie des centroïdes des secteurs agricoles

Column

Tableau de mesure de distance avec le centroïde de l’écosystème

Analyse des variables

column {data-width = 330}

Graphique des distances en fonction des secteurs d’activité

column {data-width = 330}

Graphique des distances moyennes en fonction du nombre d’employé

Graphique des ecart-types en fonction du nombre d’employé

column {data-width = 330}

Graphique des distances moyenne en fonction de l’âge des exploitations

Graphique des écart-types en fonction de l’âge des exploitations

Analyse des secteurs

Column

boxplot des distances par secteur

Column

répartition des secteurs

Repenser l’agriculture

Column

Nombre optimal de clusters

Visualisation suivant les variables d’entrées

Column

Visualisation des entreprises

Visualisation des clusters

---
title: "Innovation agricole au Québec"
author: "LRI"
output: 
  flexdashboard::flex_dashboard:
    theme : readable
    social: menu
    source: embed
    logo: polytechnique_gauche_rgb_2.jpeg
---

```{r setup, include=FALSE, echo=FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(echo = TRUE)

#    runtime: shiny
library(dplyr)
library(readxl)
library(readr)
library(ggplot2)
library(ggthemes)
library(DT)
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(tidyr)
library(plotly)
library("stringr")
library("plyr")
library("readr")
library(Req)
library(leaflet)
library(dplyr)
library(factoextra)
library(geojsonsf)
library(shiny)
library(RColorBrewer)

## On importe les données concernant l'écosystème
ecosysteme <- gsheet::gsheet2tbl("https://docs.google.com/spreadsheets/d/1XwSgrbq5P6CKFkroaciYULswxxGA0aPig1Yojcrt6tM/edit?usp=sharing")
ecosysteme$Moyenne <- as.numeric(ecosysteme$Moyenne)

## On créé la matrice de distance avec le centroïde de l'écosystème
distance <- data.frame(matrix(ncol=3, nrow=0))
colnames(distance) <- c("Categorie","Longitude","Latitude")

distance[1,] <- c(ecosysteme$Nom_ent[2],ecosysteme$Longitude[2],ecosysteme$Latitude[2])
distance$Longitude <- round(as.numeric(distance$Longitude),5)
distance$Latitude <- round(as.numeric(distance$Latitude),5)
distance[1,4] <- "Écosystème" 
distance[1,5:11] <- 1
colnames(distance)[4] <- "Secteur"
colnames(distance)[5] <- "Complet" ## Catégorie prenant tous les exploitations du secteur agricole
colnames(distance)[6] <- "emp_sup_10" ## Catégorie des exploitations de plus de 10 employés
colnames(distance)[7] <- "emp_inf_10" ## Catégorie des exploitations de moins de 10 employés
colnames(distance)[8] <- "age_inf_5" ## Catégorie des entreprises agricoles de moins de 5 ans
colnames(distance)[9] <- "age_inf_15_sup_5" ## Catégorie des entreprises agricoles de plus de 5 ans et moins de 15 ans
colnames(distance)[10] <- "age_sup_15" ## Catégorie des entreprises agricoles de plus de 15 ans
colnames(distance)[11] <- "Dist_centre" ## Distance avec le centroïde de la grappe industrielle


```

# Ecosystème

```{r, echo=FALSE, message = FALSE, warning = FALSE}
ecosysteme <- ecosysteme[-1,]

ecosysteme$Moyenne2 <- round(ecosysteme$Moyenne^2,2)

## On cartographie l'écosystème
leaflet(data = ecosysteme) %>% 
  setView(-72.8, 46.1, 8) %>%
  addTiles() %>% 
  addCircleMarkers(lng = ~Longitude, lat = ~Latitude, ## On ajoute les points de coordonnées des entreprises
      radius = ~Moyenne2, stroke = FALSE, fillOpacity = 0.8, 
      color= ~colorBin('OrRd',Moyenne)(Moyenne), 
      popup = ~ paste("Nom:", Nom_ent, "<br/>","Signal :", Moyenne,"<br/>","Impact des services :", Impact)) %>%
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 25, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue",
      popup = paste("Nom:", ecosysteme$Nom_ent[1], "<br/>","Signal :", ecosysteme$Moyenne[1],"<br/>","Impact des services :", ecosysteme$Impact[1]) )  %>%
  addLegend(
    "topleft",
    title = "Signal de l'entreprise",
    pal = colorBin('OrRd', ecosysteme$Moyenne),
    values = ecosysteme$Impact, 
    opacity = 0.9)
    
  
  
```

# Exemple : Céréales

## Column {data-width="650"}

### Carte des catégories

```{r, echo=FALSE, message=FALSE,warning=FALSE}
df_0131 <- Req::Req_data(industry = 0131, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r, echo=FALSE, message = FALSE, warning = FALSE}

distance[2,1] <- "Céréales"
distance[2,2] <- round(mean(df_0131$Long,na.rm = TRUE),5)
distance[2,3] <- round(mean(df_0131$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[2,4] <- "Céréales"
distance[2,5] <- 1
distance[2,12] <- length(df_0131$Long) ## Calcul de nombre d'entreprises agricoles appartenant à la catégorie
distance[2,13] <- round(mean(sqrt((df_0131$Lat - mean(df_0131$Lat,na.rm = TRUE))^2 + (df_0131$Long - mean(df_0131$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[2,14] <- round(sd(sqrt((df_0131$Lat - mean(df_0131$Lat,na.rm = TRUE))^2 + (df_0131$Long - mean(df_0131$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[2,15] <- round(sd(sqrt((df_0131$Lat - ecosysteme$Latitude[1])^2 + (df_0131$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0131 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[3,1] <- "Céréales & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[3,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[3,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[3,4] <- "Céréales"
distance[3,6] <- 1
distance[3,12] <- length(index$Long)
distance[3,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[3,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[3,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0131 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[4,1] <- "Céréales & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[4,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[4,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[4,4] <- "Céréales"
distance[4,7] <- 1
distance[4,12] <- length(index$Long)
distance[4,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[4,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[4,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0131 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[5,1] <- "Céréales & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 5 ans
distance[5,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[5,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[5,4] <- "Céréales"
distance[5,8] <- 1
distance[5,12] <- length(index$Long)
distance[5,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[5,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[5,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0131 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[6,1] <- "Céréales & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[6,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[6,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[6,4] <- "Céréales"
distance[6,9] <- 1
distance[6,12] <- length(index$Long)
distance[6,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[6,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[6,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0131 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[7,1] <- "Céréales & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[7,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[7,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[7,4] <- "Céréales"
distance[7,10] <- 1
distance[7,12] <- length(index$Long)
distance[7,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[7,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[7,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

```

```{r, echo=FALSE, message = FALSE, warning = FALSE}
leaflet(data = distance[-1,]) %>%
  addTiles() %>% 
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 25, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue", popup = ecosysteme$Nom_ent[1]) %>%
  addAwesomeMarkers(popup = ~Categorie)
```

## Column {data-width="350"}

### Tableau de mesure de distance avec le centroïde de l'écosystème

```{r, echo=FALSE, message = FALSE, warning = FALSE}
Long_centre <-distance$Longitude[1]
Lat_centre <- distance$Latitude[1]
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
colnames(distance)[11] <- "Dist_centre" ## On calcule la distance entre le centre de la catégorie et l'écosystème
colnames(distance)[12] <- "nb_ent" ## On compte le nombre d'entreprise remplissant les critères définis
colnames(distance)[13] <- "moyenne_dist" ## On calcule la distance moyenne entre les entreprises d'un secteur et son centre
colnames(distance)[14] <- "ecart_type_dist" ## On calcule l'écart type des distance entre les entreprises d'une catégorie donnée
colnames(distance)[15] <- "ecart_type_eco" ## On calcule l'écart type des distances entre les entreprises d'une catégorie et le centre de l'écosystème
```

```{r, echo=FALSE, message = FALSE, warning = FALSE}
datatable(distance[,c(1,11,12,15)])
```

```{r Culture du maïs (sauf le maïs fourrager et le maïs sucré) (0134) , echo=FALSE, message=FALSE,warning=FALSE}
df_0134 <- Req::Req_data(industry = 0134, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```


```{r Culture du maïs (sauf le maïs fourrager et le maïs sucré) (0134) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[8,1] <- "Maïs"
distance[8,2] <- round(mean(df_0134$Long,na.rm = TRUE),5)
distance[8,3] <- round(mean(df_0134$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[8,5] <- 1
distance[8,12] <- length(df_0134$Long)
distance[8,13] <- round(mean(sqrt((df_0134$Lat - mean(df_0134$Lat,na.rm = TRUE))^2 + (df_0134$Long - mean(df_0134$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[8,14] <- round(sd(sqrt((df_0134$Lat - mean(df_0134$Lat,na.rm = TRUE))^2 + (df_0134$Long - mean(df_0134$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[8,15] <- round(sd(sqrt((df_0134$Lat - ecosysteme$Latitude[1])^2 + (df_0134$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0134 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[9,1] <- "Maïs & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[9,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[9,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[9,6] <- 1
distance[9,12] <- length(index$Long)
distance[9,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[9,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[9,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0134 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[10,1] <- "Maïs & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[10,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[10,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[10,7] <- 1
distance[10,12] <- length(index$Long)
distance[10,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[10,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[10,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0134 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[11,1] <- "Maïs & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[11,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[11,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[11,8] <- 1
distance[11,12] <- length(index$Long)
distance[11,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[11,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[11,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0134 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[12,1] <- "Maïs & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[12,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[12,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[12,9] <- 1
distance[12,12] <- length(index$Long)
distance[12,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[12,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[12,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0134 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[13,1] <- "Maïs & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[13,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[13,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[13,10] <- 1
distance[13,12] <- length(index$Long)
distance[13,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[13,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[13,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(8:13),4] <- "Maïs"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de plantes fourragères (0135), echo=FALSE, message=FALSE,warning=FALSE}
df_0135 <- Req::Req_data(industry = 0135, active = 1)%>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de plantes fourragères (0135) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[14,1] <- "Plantes fourragères"
distance[14,2] <- round(mean(df_0135$Long,na.rm = TRUE),5)
distance[14,3] <- round(mean(df_0135$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[14,5] <- 1 
distance[14,12] <- length(df_0135$Long)
distance[14,13] <- round(mean(sqrt((df_0135$Lat - mean(df_0135$Lat,na.rm = TRUE))^2 + (df_0135$Long - mean(df_0135$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[14,14] <- round(sd(sqrt((df_0135$Lat - mean(df_0135$Lat,na.rm = TRUE))^2 + (df_0135$Long - mean(df_0135$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[14,15] <- round(sd(sqrt((df_0135$Lat - ecosysteme$Latitude[1])^2 + (df_0135$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0135 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[15,1] <- "Plantes fourragères & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[15,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[15,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[15,6] <- 1 
distance[15,12] <- length(index$Long)
distance[15,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[15,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[15,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0135 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[16,1] <- "Plantes fourragères & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[16,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[16,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[16,7] <- 1 
distance[16,12] <- length(index$Long)
distance[16,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[16,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[16,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0135 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[17,1] <- "Plantes fourragères & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[17,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[17,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[17,8] <- 1 
distance[17,12] <- length(index$Long)
distance[17,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[17,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[17,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0135 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[18,1] <- "Plantes fourragères & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[18,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[18,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[18,9] <- 1 
distance[18,12] <- length(index$Long)
distance[18,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[18,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[18,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0135 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[19,1] <- "Plantes fourragères & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[19,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[19,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[19,10] <- 1 
distance[19,12] <- length(index$Long)
distance[19,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[19,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[19,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(14:19),4] <- "Plantes fourragères"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de pommes de terre (0138) , echo=FALSE, message=FALSE,warning=FALSE}
df_0138 <- Req::Req_data(industry = 0138, active = 1)%>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de pommes de terre (0138) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[20,1] <- "Pommes de terre"
distance[20,2] <- round(mean(df_0138$Long,na.rm = TRUE),5)
distance[20,3] <- round(mean(df_0138$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[20,5] <- 1
distance[20,12] <- length(df_0138$Long)
distance[20,13] <- round(mean(sqrt((df_0138$Lat - mean(df_0138$Lat,na.rm = TRUE))^2 + (df_0138$Long - mean(df_0138$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[20,14] <- round(sd(sqrt((df_0138$Lat - mean(df_0138$Lat,na.rm = TRUE))^2 + (df_0138$Long - mean(df_0138$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[20,15] <- round(sd(sqrt((df_0138$Lat - ecosysteme$Latitude[1])^2 + (df_0138$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0138 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[21,1] <- "Pommes de terre & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[21,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[21,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[21,6] <- 1
distance[21,12] <- length(index$Long)
distance[21,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[21,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[21,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0138 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[22,1] <- "Pommes de terre & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[22,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[22,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[22,7] <- 1
distance[22,12] <- length(index$Long)
distance[22,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[22,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[22,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0138 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[23,1] <- "Pommes de terre & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[23,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[23,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[23,8] <- 1
distance[23,12] <- length(index$Long)
distance[23,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[23,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[23,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0138 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[24,1] <- "Pommes de terre & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[24,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[24,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[24,9] <- 1
distance[24,12] <- length(index$Long)
distance[24,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[24,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[24,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0138 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[25,1] <- "Pommes de terre & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[25,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[25,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[25,10] <- 1
distance[25,12] <- length(index$Long)
distance[25,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[25,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[25,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(20:25),4] <- "Pommes de terre"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Autres grandes cultures (0139) , echo=FALSE, message=FALSE,warning=FALSE}
df_0139 <- Req::Req_data(industry = 0139, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Autres grandes cultures (0139) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[26,1] <- "Autres grandes cultures"
distance[26,2] <- round(mean(df_0139$Long,na.rm = TRUE),5)
distance[26,3] <- round(mean(df_0139$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[26,5] <- 1
distance[26,12] <- length(df_0139$Long)
distance[26,13] <- round(mean(sqrt((df_0139$Lat - mean(df_0139$Lat,na.rm = TRUE))^2 + (df_0139$Long - mean(df_0139$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[26,14] <- round(sd(sqrt((df_0139$Lat - mean(df_0139$Lat,na.rm = TRUE))^2 + (df_0139$Long - mean(df_0139$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[26,15] <- round(sd(sqrt((df_0139$Lat - ecosysteme$Latitude[1])^2 + (df_0139$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)
  
index <- df_0139 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[27,1] <- "Autres grandes cultures & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[27,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[27,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[27,6] <- 1
distance[27,12] <- length(index$Long)
distance[27,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[27,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[27,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0139 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[28,1] <- "Autres grandes cultures & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[28,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[28,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[28,7] <- 1
distance[28,12] <- length(index$Long)
distance[28,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[28,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[28,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0139 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[29,1] <- "Autres grandes cultures & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[29,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[29,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[29,8] <- 1
distance[29,12] <- length(index$Long)
distance[29,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[29,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[29,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0139 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[30,1] <- "Autres grandes cultures & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[30,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[30,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[30,9] <- 1
distance[30,12] <- length(index$Long)
distance[30,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[30,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[30,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0139 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[31,1] <- "Autres grandes cultures & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[31,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[31,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[31,10] <- 1
distance[31,12] <- length(index$Long)
distance[31,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[31,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[31,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(26:31),4] <- "Autres grandes cultures"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de fruits (0151) , echo=FALSE, message=FALSE,warning=FALSE}
df_0151 <- Req::Req_data(industry = 0151, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de fruits (0151) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[32,1] <- "Fruits"
distance[32,2] <- round(mean(df_0151$Long,na.rm = TRUE),5)
distance[32,3] <- round(mean(df_0151$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[32,5] <- 1
distance[32,12] <- length(df_0151$Long)
distance[32,13] <- round(mean(sqrt((df_0151$Lat - mean(df_0151$Lat,na.rm = TRUE))^2 + (df_0151$Long - mean(df_0151$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[32,14] <- round(sd(sqrt((df_0151$Lat - mean(df_0151$Lat,na.rm = TRUE))^2 + (df_0151$Long - mean(df_0151$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[32,15] <- round(sd(sqrt((df_0151$Lat - ecosysteme$Latitude[1])^2 + (df_0151$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0151 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[33,1] <- "Fruits & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[33,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[33,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[33,6] <- 1
distance[33,12] <- length(index$Long)
distance[33,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[33,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[33,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0151 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[34,1] <- "Fruits & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[34,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[34,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[34,7] <- 1
distance[34,12] <- length(index$Long)
distance[34,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[34,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[34,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0151 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[35,1] <- "Fruits & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[35,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[35,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[35,8] <- 1
distance[35,12] <- length(index$Long)
distance[35,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[35,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[35,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0151 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[36,1] <- "Fruits & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[36,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[36,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[36,9] <- 1
distance[36,12] <- length(index$Long)
distance[36,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[36,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[36,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0151 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[37,1] <- "Fruits & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[37,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[37,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[37,10] <- 1
distance[37,12] <- length(index$Long)
distance[37,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[37,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[37,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(32:37),4] <- "Fruits"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de légumes (0152) , echo=FALSE, message=FALSE,warning=FALSE}
df_0152 <- Req::Req_data(industry = 0152, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de légumes (0152) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[38,1] <- "Légumes"
distance[38,2] <- round(mean(df_0152$Long,na.rm = TRUE),5)
distance[38,3] <- round(mean(df_0152$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[38,5] <- 1 
distance[38,12] <- length(df_0152$Long)
distance[38,13] <- round(mean(sqrt((df_0152$Lat - mean(df_0152$Lat,na.rm = TRUE))^2 + (df_0152$Long - mean(df_0152$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[38,14] <- round(sd(sqrt((df_0152$Lat - mean(df_0152$Lat,na.rm = TRUE))^2 + (df_0152$Long - mean(df_0152$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[38,15] <- round(sd(sqrt((df_0152$Lat - ecosysteme$Latitude[1])^2 + (df_0152$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0152 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[39,1] <- "Légumes & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[39,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[39,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[39,6] <- 1 
distance[39,12] <- length(index$Long)
distance[39,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[39,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[39,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0152 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[40,1] <- "Légumes & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[40,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[40,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[40,7] <- 1 
distance[40,12] <- length(index$Long)
distance[40,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[40,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[40,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0152 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[41,1] <- "Légumes & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[41,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[41,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[41,8] <- 1 
distance[41,12] <- length(index$Long)
distance[41,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[41,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[41,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0152 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[42,1] <- "Légumes & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[42,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[42,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[42,9] <- 1 
distance[42,12] <- length(index$Long)
distance[42,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[42,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[42,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0152 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[43,1] <- "Légumes & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[43,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[43,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[43,10] <- 1 
distance[43,12] <- length(index$Long)
distance[43,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[43,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[43,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(38:43),4] <- "Légumes"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture mixte de fruits et légumes (0159) , echo=FALSE, message=FALSE,warning=FALSE}
df_0159 <- Req::Req_data(industry = 0159, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture mixte de fruits et légumes (0159) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[44,1] <- "Mixte fruits et légumes"
distance[44,2] <- round(mean(df_0159$Long,na.rm = TRUE),5)
distance[44,3] <- round(mean(df_0159$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[44,5] <- 1 
distance[44,12] <- length(df_0159$Long)
distance[44,13] <- round(mean(sqrt((df_0159$Lat - mean(df_0159$Lat,na.rm = TRUE))^2 + (df_0159$Long - mean(df_0159$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[44,14] <- round(sd(sqrt((df_0159$Lat - mean(df_0159$Lat,na.rm = TRUE))^2 + (df_0159$Long - mean(df_0159$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[44,15] <- round(sd(sqrt((df_0159$Lat - ecosysteme$Latitude[1])^2 + (df_0159$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0159 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[45,1] <- "Mixte fruits et légumes & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[45,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[45,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[45,6] <- 1 
distance[45,12] <- length(index$Long)
distance[45,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[45,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[45,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0159 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[46,1] <- "Mixte fruits et légumes & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[46,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[46,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[46,7] <- 1 
distance[46,12] <- length(index$Long)
distance[46,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[46,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[46,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0159 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[47,1] <- "Mixte fruits et légumes & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[47,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[47,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[47,8] <- 1 
distance[47,12] <- length(index$Long)
distance[47,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[47,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[47,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0159 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[48,1] <- "Mixte fruits et légumes & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[48,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[48,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[48,9] <- 1 
distance[48,12] <- length(index$Long)
distance[48,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[48,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[48,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0159 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[49,1] <- "Mixte fruits et légumes & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[49,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[49,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[49,10] <- 1 
distance[49,12] <- length(index$Long)
distance[49,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[49,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[49,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(44:49),4] <- "Mixte fruits et légumes"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture de champignons (0161) , echo=FALSE, message=FALSE,warning=FALSE}
df_0161 <- Req::Req_data(industry = 0161, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture de champignons (0161) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[50,1] <- "Champignons"
distance[50,2] <- round(mean(df_0161$Long,na.rm = TRUE),5)
distance[50,3] <- round(mean(df_0161$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[50,5] <- 1
distance[50,12] <- length(df_0161$Long)
distance[50,13] <- round(mean(sqrt((df_0161$Lat - mean(df_0161$Lat,na.rm = TRUE))^2 + (df_0161$Long - mean(df_0161$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[50,14] <- round(sd(sqrt((df_0161$Lat - mean(df_0161$Lat,na.rm = TRUE))^2 + (df_0161$Long - mean(df_0161$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[50,15] <- round(sd(sqrt((df_0161$Lat - ecosysteme$Latitude[1])^2 + (df_0161$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0161 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[51,1] <- "Champignons & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[51,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[51,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[51,6] <- 1
distance[51,12] <- length(index$Long)
distance[51,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[51,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[51,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0161 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[52,1] <- "Champignons & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[52,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[52,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[52,7] <- 1
distance[52,12] <- length(index$Long)
distance[52,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[52,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[52,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0161 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[53,1] <- "Champignons & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[53,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[53,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[53,8] <- 1
distance[53,12] <- length(index$Long)
distance[53,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[53,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[53,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0161 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[54,1] <- "Champignons & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[54,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[54,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[54,9] <- 1
distance[54,12] <- length(index$Long)
distance[54,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[54,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[54,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0161 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[55,1] <- "Champignons & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[55,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[55,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[55,10] <- 1
distance[55,12] <- length(index$Long)
distance[55,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[55,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[55,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(50:55),4] <- "Champignons"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture en serres (0162) , echo=FALSE, message=FALSE,warning=FALSE}
df_0162 <- Req::Req_data(industry = 0162, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture en serres (0162) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[56,1] <- "Serres"
distance[56,2] <- round(mean(df_0162$Long,na.rm = TRUE),5)
distance[56,3] <- round(mean(df_0162$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[56,5] <- 1
distance[56,12] <- length(df_0162$Long)
distance[56,13] <- round(mean(sqrt((df_0162$Lat - mean(df_0162$Lat,na.rm = TRUE))^2 + (df_0162$Long - mean(df_0162$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[56,14] <- round(sd(sqrt((df_0162$Lat - mean(df_0162$Lat,na.rm = TRUE))^2 + (df_0162$Long - mean(df_0162$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[56,15] <- round(sd(sqrt((df_0162$Lat - ecosysteme$Latitude[1])^2 + (df_0162$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0162 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[57,1] <- "Serres & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[57,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[57,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[57,6] <- 1
distance[57,12] <- length(index$Long)
distance[57,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[57,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[57,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0162 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[58,1] <- "Serres & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[58,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[58,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[58,7] <- 1
distance[58,12] <- length(index$Long)
distance[58,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[58,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[58,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0162 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[59,1] <- "Serres & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[59,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[59,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[59,8] <- 1
distance[59,12] <- length(index$Long)
distance[59,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[59,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[59,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0162 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[60,1] <- "Serres & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[60,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[60,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[60,9] <- 1
distance[60,12] <- length(index$Long)
distance[60,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[60,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[60,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0162 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[61,1] <- "Serres & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[61,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[61,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[61,10] <- 1
distance[61,12] <- length(index$Long)
distance[61,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[61,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[61,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(56:61),4] <- "Serres"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture en pépinières & gazonnières (0163) , echo=FALSE, message=FALSE,warning=FALSE}
df_0163 <- Req::Req_data(industry = 0163, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture en pépinières & gazonnières (0163) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[62,1] <- "Pépinières"
distance[62,2] <- round(mean(df_0163$Long,na.rm = TRUE),5)
distance[62,3] <- round(mean(df_0163$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[62,5] <- 1
distance[62,12] <- length(df_0163$Long)
distance[62,13] <- round(mean(sqrt((df_0163$Lat - mean(df_0163$Lat,na.rm = TRUE))^2 + (df_0163$Long - mean(df_0163$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[62,14] <- round(sd(sqrt((df_0163$Lat - mean(df_0163$Lat,na.rm = TRUE))^2 + (df_0163$Long - mean(df_0163$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[62,15] <- round(sd(sqrt((df_0163$Lat - ecosysteme$Latitude[1])^2 + (df_0163$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0163 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[63,1] <- "Pépinières & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[63,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[63,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[63,6] <- 1
distance[63,12] <- length(index$Long)
distance[63,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[63,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[63,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0163 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[64,1] <- "Pépinières & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[64,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[64,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[64,7] <- 1
distance[64,12] <- length(index$Long)
distance[64,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[64,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[64,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0163 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[65,1] <- "Pépinières & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[65,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[65,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[65,8] <- 1
distance[65,12] <- length(index$Long)
distance[65,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[65,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[65,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0163 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[66,1] <- "Pépinières & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[66,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[66,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[66,9] <- 1
distance[66,12] <- length(index$Long)
distance[66,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[66,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[66,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0163 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[67,1] <- "Pépinières & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[67,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[67,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[67,10] <- 1
distance[67,12] <- length(index$Long)
distance[67,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[67,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[67,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(62:67),4] <- "Pépinières"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Culture érabilières (0164) , echo=FALSE, message=FALSE,warning=FALSE}
df_0164 <- Req::Req_data(industry = 0164, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Culture érabilières (0164) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[68,1] <- "Érablières"
distance[68,2] <- round(mean(df_0164$Long,na.rm = TRUE),5)
distance[68,3] <- round(mean(df_0164$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[68,5] <- 1
distance[68,12] <- length(df_0164$Long)
distance[68,13] <- round(mean(sqrt((df_0164$Lat - mean(df_0164$Lat,na.rm = TRUE))^2 + (df_0164$Long - mean(df_0164$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[68,14] <- round(sd(sqrt((df_0164$Lat - mean(df_0164$Lat,na.rm = TRUE))^2 + (df_0164$Long - mean(df_0164$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[68,15] <- round(sd(sqrt((df_0164$Lat - ecosysteme$Latitude[1])^2 + (df_0164$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0164 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[69,1] <- "Érablières & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[69,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[69,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[69,6] <- 1
distance[69,12] <- length(index$Long)
distance[69,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[69,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[69,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0164 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[70,1] <- "Érablières & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[70,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[70,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[70,7] <- 1
distance[70,12] <- length(index$Long)
distance[70,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[70,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[70,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0164 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[71,1] <- "Érablières & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[71,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[71,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[71,8] <- 1
distance[71,12] <- length(index$Long)
distance[71,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[71,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[71,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0164 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[72,1] <- "Érablières & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[72,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[72,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[72,9] <- 1
distance[72,12] <- length(index$Long)
distance[72,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[72,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[72,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0164 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[73,1] <- "Érablières & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[73,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[73,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[73,10] <- 1
distance[73,12] <- length(index$Long)
distance[73,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[73,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[73,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(68:73),4] <- "Érablières"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Autres spécialités horticoles (0169) , echo=FALSE, message=FALSE,warning=FALSE}
df_0169 <- Req::Req_data(industry = 0169, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API

```

```{r Autres spécialités horticoles (0169) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[74,1] <- "Autres cultures horticoles"
distance[74,2] <- round(mean(df_0169$Long,na.rm = TRUE),5)
distance[74,3] <- round(mean(df_0169$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[74,5] <- 1
distance[74,12] <- length(df_0169$Long)
distance[74,13] <- round(mean(sqrt((df_0169$Lat - mean(df_0169$Lat,na.rm = TRUE))^2 + (df_0169$Long - mean(df_0169$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[74,14] <- round(sd(sqrt((df_0169$Lat - mean(df_0169$Lat,na.rm = TRUE))^2 + (df_0169$Long - mean(df_0169$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[74,15] <- round(sd(sqrt((df_0169$Lat - ecosysteme$Latitude[1])^2 + (df_0169$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0169 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[75,1] <- "Autres cultures horticoles & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[75,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[75,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[75,6] <- 1
distance[75,12] <- length(index$Long)
distance[75,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[75,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[75,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0169 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[76,1] <- "Autres cultures horticoles & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[76,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[76,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[76,7] <- 1
distance[76,12] <- length(index$Long)
distance[76,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[76,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[76,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0169 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[77,1] <- "Autres cultures horticoles & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[77,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[77,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[77,8] <- 1
distance[77,12] <- length(index$Long)
distance[77,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[77,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[77,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0169 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[78,1] <- "Autres cultures horticoles & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[78,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[78,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[78,9] <- 1
distance[78,12] <- length(index$Long)
distance[78,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[78,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[78,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0169 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[79,1] <- "Autres cultures horticoles & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[79,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[79,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[79,10] <- 1
distance[79,12] <- length(index$Long)
distance[79,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[79,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[79,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(74:79),4] <- "Autres cultures horticoles"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r Élevages grandes cultures et productions horticoles (0171) , echo=FALSE, message=FALSE,warning=FALSE}
df_0171 <- Req::Req_data(industry = 0171, active = 1) %>%
  filter(province == "Quebec") ## On récupères les données de la REQ par API
```

```{r Élevages grandes cultures et productions horticoles (0171) 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[80,1] <- "Élevages, grandes cultures et productions horticoles"
distance[80,2] <- round(mean(df_0171$Long,na.rm = TRUE),5)
distance[80,3] <- round(mean(df_0171$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[80,5] <- 1
distance[80,12] <- length(df_0171$Long)
distance[80,13] <- round(mean(sqrt((df_0171$Lat - mean(df_0171$Lat,na.rm = TRUE))^2 + (df_0171$Long - mean(df_0171$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[80,14] <- round(sd(sqrt((df_0171$Lat - mean(df_0171$Lat,na.rm = TRUE))^2 + (df_0171$Long - mean(df_0171$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[80,15] <- round(sd(sqrt((df_0171$Lat - ecosysteme$Latitude[1])^2 + (df_0171$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)


index <- df_0171 %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[81,1] <- "Élevages, grandes cultures et productions horticoles & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[81,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[81,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[81,6] <- 1
distance[81,12] <- length(index$Long)
distance[81,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[81,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[81,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0171 %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[82,1] <- "Élevages, grandes cultures et productions horticoles & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[82,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[82,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[82,7] <- 1
distance[82,12] <- length(index$Long)
distance[82,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[82,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[82,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0171 %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[83,1] <- "Élevages, grandes cultures et productions horticoles & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[83,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[83,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[83,8] <- 1
distance[83,12] <- length(index$Long)
distance[83,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[83,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[83,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0171 %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[84,1] <- "Élevages, grandes cultures et productions horticoles & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[84,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[84,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[84,9] <- 1
distance[84,12] <- length(index$Long)
distance[84,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[84,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[84,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_0171 %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[85,1] <- "Élevages, grandes cultures et productions horticoles & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[85,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[85,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[85,10] <- 1
distance[85,12] <- length(index$Long)
distance[85,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[85,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[85,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(80:85),4] <- "Élev., grandes cult. et prod. hort."
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

```{r echo=FALSE, message = FALSE, warning = FALSE}
#.sidebar
# selectInput("sect_act", label = "Secteur d'activité",
#            choices = distance$Secteur, selected = distance$Secteur[1])

```

```{r echo=FALSE, message = FALSE, warning = FALSE}

##dist_user <- reactive({distance %>%
  #filter(Secteur ==input$sect_act)
  #})

#renderLeaflet({
 # leaflet(data = distance[which(distance$Secteur == input$sect_act),]) %>%
  #addTiles() %>%
  #addAwesomeMarkers(popup = ~Categorie)
#})
```

```{r df exploitations agricoles cumulees, echo=FALSE, message = FALSE, warning = FALSE}

df_tot <- full_join(df_0131,df_0134)
df_tot <- full_join(df_tot,df_0135)
df_tot <- full_join(df_tot,df_0138)
df_tot <- full_join(df_tot,df_0139)
df_tot <- full_join(df_tot,df_0151)
df_tot <- full_join(df_tot,df_0152)
df_tot <- full_join(df_tot,df_0159)
df_tot <- full_join(df_tot,df_0161)
df_tot <- full_join(df_tot,df_0162)
df_tot <- full_join(df_tot,df_0163)
df_tot <- full_join(df_tot,df_0164)
df_tot <- full_join(df_tot,df_0169)
df_tot <- full_join(df_tot,df_0171)
```

```{r exploitations agricoles cumulees 2, echo=FALSE, message = FALSE, warning = FALSE}

distance[86,1] <- "Total des exploitations agricoles"
distance[86,2] <- round(mean(df_tot$Long,na.rm = TRUE),5)
distance[86,3] <- round(mean(df_tot$Lat,na.rm = TRUE),5) ## On ajoute les coordonnées des centroïdes du secteur d'activité dans la matrice de distance
distance[86,5] <- 1
distance[86,12] <- length(df_tot$Long)
distance[86,13] <- round(mean(sqrt((df_tot$Lat - mean(df_tot$Lat,na.rm = TRUE))^2 + (df_tot$Long - mean(df_tot$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[86,14] <- round(sd(sqrt((df_tot$Lat - mean(df_tot$Lat,na.rm = TRUE))^2 + (df_tot$Long - mean(df_tot$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[86,15] <- round(sd(sqrt((df_tot$Lat - ecosysteme$Latitude[1])^2 + (df_tot$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_tot %>%
  filter(COD_INTVAL_EMPLO_QUE != "N" & COD_INTVAL_EMPLO_QUE != "O"
         & COD_INTVAL_EMPLO_QUE != "A" 
         & COD_INTVAL_EMPLO_QUE != "B" )
distance[87,1] <- "Total des exploitations agricoles & emp > 10" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 10 employés
distance[87,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[87,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[87,6] <- 1
distance[87,12] <- length(index$Long)
distance[87,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[87,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[87,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_tot %>%
  filter(COD_INTVAL_EMPLO_QUE != "E" & COD_INTVAL_EMPLO_QUE != "D" & COD_INTVAL_EMPLO_QUE != "C")
distance[88,1] <- "Total des exploitations agricoles & emp < 10" ## On ajoute la catégorie des entreprises agricoles céréalières de moins de 10 employés
distance[88,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[88,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[88,7] <- 1
distance[88,12] <- length(index$Long)
distance[88,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[88,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[88,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_tot %>%
  filter(DAT_STAT_IMMAT > "2018-01-01")
distance[89,1] <- "Total des exploitations agricoles & age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans
distance[89,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[89,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[89,8] <- 1
distance[89,12] <- length(index$Long)
distance[89,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[89,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[89,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_tot %>%
  filter(DAT_STAT_IMMAT < "2018-01-01" & DAT_STAT_IMMAT > "2008-01-01" )
distance[90,1] <- "Total des exploitations agricoles & 15 < age < 5" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 5 ans et moins de 15 ans
distance[90,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[90,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[90,9] <- 1
distance[90,12] <- length(index$Long)
distance[90,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[90,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[90,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

index <- df_tot %>%
  filter(DAT_STAT_IMMAT < "2008-01-01")
distance[91,1] <- "Total des exploitations agricoles & age > 15" ## On ajoute la catégorie des entreprises agricoles céréalières de plus de 15 ans
distance[91,2] <- round(mean(index$Long,na.rm = TRUE),5)
distance[91,3] <- round(mean(index$Lat,na.rm = TRUE),5)
distance[91,10] <- 1
distance[91,12] <- length(index$Long)
distance[91,13] <- round(mean(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2),na.rm=TRUE),4)
distance[91,14] <- round(sd(sqrt((index$Lat - mean(index$Lat,na.rm = TRUE))^2 + (index$Long - mean(index$Long,na.rm = TRUE))^2), na.rm = TRUE),4)
distance[91,15] <- round(sd(sqrt((index$Lat - ecosysteme$Latitude[1])^2 + (index$Long - ecosysteme$Longitude[1])^2),na.rm = TRUE),4)

##leaflet(data = distance) %>%
  #addTiles() %>% 
  #addAwesomeMarkers(popup = ~Secteur)
distance[c(86:91),4] <- "Total des exploitations agricoles"
distance[,11] <- round(sqrt((distance$Longitude - Long_centre)^2 + (distance$Latitude - Lat_centre)^2),4)
```

# Cartographie des secteurs

## Column {data-width="650"}

### Cartographie des centroïdes des secteurs agricoles

```{r, echo=FALSE, message = FALSE, warning = FALSE}

index <- distance[-1,] %>%
  filter(Complet == 1)

leaflet(data = index) %>%
  addTiles() %>% 
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 15, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue", popup = ecosysteme$Nom_ent[1]) %>%
  
    addCircleMarkers(data = index[!index$Secteur %in% "Total des exploitations agricoles",],
                   lng = ~Longitude, lat = ~Latitude, color ="#D7301F",stroke = FALSE, fillOpacity = 0.8,
                   popup = ~paste("Nom :", Categorie, "<br/>","Distance avec l'écosystème", Dist_centre)) %>%
  
  addCircleMarkers(data = index[index$Secteur %in% "Total des exploitations agricoles",],
                   lng = ~Longitude, lat = ~Latitude, color = "#FC8D59" , stroke = FALSE, fillOpacity = 0.9, radius = 12,
                   popup = ~paste("Nom :", Categorie, "<br/>","Distance avec l'écosystème", Dist_centre)) %>%
      addLegend(
    "topleft",
    title = "Légende",
    colors = c("steelblue","#D7301F","#FC8D59"),
   labels = c("Centre de l'écosystème d'innovation", "Centre des secteurs agricoles","centre du total des exploitations agricoles"), 
    opacity = 0.9)

```

## Column {data-width="350"}

### Tableau de mesure de distance avec le centroïde de l'écosystème

```{r echo=FALSE, message = FALSE, warning = FALSE}
datatable(index[,c(1,11,15)])
```

# Analyse des variables

## column {data-width = 330}

### Graphique des distances en fonction des secteurs d'activité

```{r echo=FALSE, message = FALSE, warning = FALSE}
distance[] %>%
  filter(Complet==1) %>%
  ggplot() +
    geom_point(aes(Secteur,Dist_centre,color="red")) +
    geom_point(aes(Secteur,ecart_type_eco,color = "blue" )) +
    theme_minimal() +
    ylab("Valeur") +
    labs(colour = "Légende") +
    scale_color_manual(values = c("red","steelblue"),
                       labels = c("Ecart-type","Distance moyenne")) +
    theme(axis.text.x = element_text(angle = 90))
```

## column {data-width = 330}

### Graphique des distances moyennes en fonction du nombre d'employé

```{r echo=FALSE, message = FALSE, warning = FALSE}
  ggplot() +
    geom_point(data = distance[distance$emp_sup_10==1,],
               aes(Secteur,Dist_centre,color="orange")) +
    geom_point(data = distance[distance$emp_inf_10==1,],
               aes(Secteur,Dist_centre,color="darkgreen")) +
    labs(colour = "Distance moyenne") +
    scale_color_manual(values = c("darkgreen","orange"),
                       labels = c("< 10 employés", "> 10 employés" )) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
```



### Graphique des ecart-types en fonction du nombre d'employé

```{r echo=FALSE, message = FALSE, warning = FALSE}
  ggplot() +
    geom_point(data = distance[distance$emp_sup_10==1,],
               aes(Secteur,ecart_type_eco,color = "orange" )) +
    geom_point(data = distance[distance$emp_inf_10==1,],
               aes(Secteur,ecart_type_eco,color = "darkgreen" )) +
    labs(colour = "Écart-type") +
    scale_color_manual(values = c("darkgreen","orange"),
                       labels = c("< 10 employés", "> 10 employés")) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
```

## column {data-width = 330}

### Graphique des distances moyenne en fonction de l'âge des exploitations

```{r echo=FALSE, message = FALSE, warning = FALSE}
  ggplot() +
    geom_point(data = distance[distance$age_inf_5==1,],
               aes(Secteur,Dist_centre,color="orange")) +
    geom_point(data = distance[distance$age_inf_15_sup_5==1,],
               aes(Secteur,Dist_centre,color="darkgreen")) +
      geom_point(data = distance[distance$age_sup_15==1,],
               aes(Secteur,Dist_centre,color="red")) +
    labs(colour = "Distance moyenne") +
    scale_color_manual(values = c("red","darkgreen","orange"),
                       labels = c("âge < 5 ans","5 ans < âge < 15 ans", "âge > 15 ans" )) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
```

### Graphique des écart-types en fonction de l'âge des exploitations

```{r echo=FALSE, message = FALSE, warning = FALSE}

  ggplot() +
    geom_point(data = distance[distance$age_inf_5==1,],
               aes(Secteur,ecart_type_eco,color="orange")) +
    geom_point(data = distance[distance$age_inf_15_sup_5==1,],
               aes(Secteur,ecart_type_eco,color="darkgreen")) +
      geom_point(data = distance[distance$age_sup_15==1,],
               aes(Secteur,ecart_type_eco,color="red")) +
    labs(colour = "Écart-type") +
    scale_color_manual(values = c("red","darkgreen","orange"),
                       labels = c("âge < 5 ans", "5 ans < âge < 15 ans", "âge > 15 ans")) +
    theme_minimal() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
```

# Analyse des secteurs

## Column {data-width="500"}

### boxplot des distances par secteur
```{r echo=FALSE, message = FALSE, warning = FALSE}
p <- ggplot(data = distance,aes(Secteur,Dist_centre) ) +
    geom_boxplot() +
    theme_economist() +
    theme(axis.text.x = element_text(angle = 90))

ggplotly(p)

```

## Column {data-width="500"}



### répartition des secteurs
```{r echo=FALSE, message = FALSE, warning = FALSE}
distance%>%
  filter(Complet==1 & nb_ent<=10000) %>%
ggplot()+
    geom_point(aes(Secteur,nb_ent))+
    theme_economist() +
    ylab("Valeur") +
    theme(axis.text.x = element_text(angle = 90))
```


# Repenser l'agriculture

## Column {data-width="350"}

### Nombre optimal de clusters
```{r preparation cluster 1, echo=FALSE, message = FALSE, warning = FALSE}
df_cluster <- df_tot[,c("NOM_ASSUJ","COD_ACT_ECON_CAE","COD_ACT_ECON_CAE2","Long","Lat")]
df_cluster <- df_cluster[!duplicated(df_cluster$NOM_ASSUJ),]
rownames(df_cluster) <-df_cluster$NOM_ASSUJ
fviz_nbclust(df_cluster[,-c(1:3)], kmeans,method ="wss")

```

### Visualisation suivant les variables d'entrées
```{r preparation cluster 2, echo=FALSE, message = FALSE, warning = FALSE,include=FALSE}
set.seed(123)
cluster1 <- kmeans(df_cluster[,-c(1:3)],6, iter.max = 10, nstart = 2)
df_cluster <- cbind(df_cluster, cluster1$cluster)
colnames(df_cluster)[6] <-"cluster"
```

```{r preparation cluster 3, echo=FALSE, message = FALSE, warning = FALSE,include=FALSE}
distance_cluster <- as.data.frame(cluster1$centers)
distance_cluster[3] <- cluster1$size
distance_cluster[4] <- round(cluster1$withinss,4)
distance_cluster$Var1 <- c(1:6)
distance_cluster$Var1 <- as.factor(distance_cluster$Var1)
colnames(distance_cluster)[3] <- "size"  
colnames(distance_cluster)[4] <- "withinss"  

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 131 | COD_ACT_ECON_CAE2 == 131) 
distance_cluster$v131 <- table(index$cluster)


index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 134 | COD_ACT_ECON_CAE2 == 134) 
distance_cluster <- left_join(distance_cluster,as.data.frame(table(index$cluster)),by="Var1")
colnames(distance_cluster)[7] <- "v134"
distance_cluster$v134[3:4] <-0

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 135 | COD_ACT_ECON_CAE2 == 135) 
distance_cluster$v135 <- 0
distance_cluster$v135 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 138 | COD_ACT_ECON_CAE2 == 138) 
distance_cluster$v138 <- 0
distance_cluster$v138 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 139 | COD_ACT_ECON_CAE2 == 139) 
distance_cluster$v139 <- 0
distance_cluster$v139 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 151 | COD_ACT_ECON_CAE2 == 151) 
distance_cluster$v151 <- 0
distance_cluster$v151 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 152 | COD_ACT_ECON_CAE2 == 152)
distance_cluster$v152 <- 0
distance_cluster$v152 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 159 | COD_ACT_ECON_CAE2 == 159) 
distance_cluster$v159 <- 0
distance_cluster$v159 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 161 | COD_ACT_ECON_CAE2 == 161) 
distance_cluster$v161 <- 0
distance_cluster$v161 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 162 | COD_ACT_ECON_CAE2 == 162)
distance_cluster$v162 <- 0
distance_cluster$v162 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 163 | COD_ACT_ECON_CAE2 == 163) 
distance_cluster$v163 <- 0
distance_cluster$v163 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 164 | COD_ACT_ECON_CAE2 == 164) 
distance_cluster$v164 <- 0
distance_cluster$v164 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 169 | COD_ACT_ECON_CAE2 == 169) 
distance_cluster$v169 <- 0
distance_cluster$v169 <- table(index$cluster)

index <- df_cluster %>%
  filter(COD_ACT_ECON_CAE == 171 | COD_ACT_ECON_CAE2 == 171) 
distance_cluster$v171 <- 0
distance_cluster$v171 <- table(index$cluster)

index <- as.data.frame(t(distance_cluster)) 
index <- index[-c(1:5),]
index$V1 <- as.numeric(index$V1)
index$V2 <- as.numeric(index$V2)
index$V3 <- as.numeric(index$V3)
index$V4 <- as.numeric(index$V4)
index$V5 <- as.numeric(index$V5)
index$V6 <- as.numeric(index$V6)
distance_cluster$sum <- colSums(index)


for (i in 6:19) {
  distance_cluster[,i] <- round(distance_cluster[,i] / distance_cluster$sum,4)
}

long_cluster <- gather(distance_cluster, key="Secteur", value = "pourcentage", -Long,-Lat,-size,-withinss, -Var1,-sum)

```


```{r, echo=FALSE, message = FALSE, warning = FALSE}
nb.cols <- 15
mycolors <- colorRampPalette(brewer.pal(8, "BrBG"))(nb.cols)

p <- ggplot(long_cluster) +
  geom_bar(aes(x=Var1,y=pourcentage,fill =Secteur),stat="identity") +
  coord_flip() +
  xlab("Numero de cluster") +
  theme_minimal() +
  scale_fill_manual(values = mycolors)

ggplotly(p)%>% #Ploty du graph
  config(displayModeBar = FALSE)
```

## Column {data-width="650"}

```{r, echo=FALSE, message = FALSE, warning = FALSE, include = FALSE}
getColor <- function(df_cluster) {
  sapply(df_cluster$cluster, function(cluster) {
  if(cluster == 1) {
    mycolors[1]
    } else if(cluster == 2) {
    mycolors[4]
  } else if(cluster == 3) {
    mycolors[6]
  } else if(cluster == 4) { 
    mycolors[11]
  } else if(cluster == 5) { 
    mycolors[13]
  } else if(cluster == 6) { 
    mycolors[15]
  } })
}

df_cluster[7] <- getColor(df_cluster)
colnames(df_cluster)[7] <- "couleur"

```

### Visualisation des entreprises
```{r visualisation cluster 1, echo=FALSE, message = FALSE, warning = FALSE}

leaflet(data = df_cluster) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~Long, lat = ~Lat, color = df_cluster$couleur, popup = ~rownames(df_cluster)) %>%
  addLegend(
    "topleft",
    title = "Appartenance des entreprises",
    colors = unique(df_cluster$couleur),
    labels = unique(df_cluster$cluster),
    opacity = 0.9)

```



### Visualisation des clusters
```{r, echo=FALSE, message = FALSE, warning = FALSE}

#table(df_cluster$COD_ACT_ECON_CAE2,df_cluster$cluster)



leaflet(data=distance_cluster) %>%
  addTiles() %>%
  addCircleMarkers(lng = ~Long, lat = ~Lat,radius = ~sqrt(size),
                   color =~colorBin("BrBG",withinss)(withinss), stroke = TRUE, fillOpacity = 0.7,
                   popup = ~paste("Sum of Square:",withinss, "<br/>","taille du cluster:", size)) %>%
  addCircleMarkers(lng = ecosysteme$Longitude[1], lat = ecosysteme$Latitude[1], ## On ajoute le centroïde de l'écosystème
      radius = 15, stroke = FALSE, fillOpacity = 0.9, 
      color = "steelblue", popup = ecosysteme$Nom_ent[1]) %>%
  addLegend(
    "topleft",
    title = "Erreur résiduelle du cluster",
    pal = colorBin('BrBG', distance_cluster$withinss),
    values = distance_cluster$withinss, 
    opacity = 0.9)
```